1. Field of the Invention
The present invention relates to computerized simulation of hydrocarbon reservoirs in the earth, and in particular to determination of reservoir attributes or properties as reservoir models when there is a limited amount of well log data available.
2. Description of the Related Art
Predicting reservoir properties out of seismic attributes where only a few wells are present in the reservoir has been a common challenge in oil industry to provide an initial reservoir characterization for reservoir modeling and evaluation. Conventionally, geo-statistics and neural networks have been used to predict well log properties from combinations of various seismic attributes. Known well-to-seismic ties have been used to learn the relationship between the seismic data and the well values. Recently, multiple seismic attributes have been used to predict well log properties via modeling techniques based on learning the relationship between the wells and seismic attributes. However, for small populations (i.e., only a few well-seismic attribute pairs), statistical significance has in some cases been impossible to achieve. The use of this technique was, however, dependent on or limited by the number of wells actually present in the reservoir.
Due to the limited availability of drilled wells, the reservoir petro-physical characteristic modeling has typically been plagued by uncertainties. Neural network methods have been developed for reservoir prediction using seismic attributes. Such methods have been based, for example, on back propagation or BP neural networks and self-organizing map or SOM neural networks to predict reservoir hydrocarbons. However, attempts to predict reservoir properties based on a neural network modeling methodology have resulted in networks which have been what is known as easily “over-trained,” which in turn has resulted in “over-fitting,” and thus provided poor predictions in validation trials.
Seismic attributes are quantitative measure of the characteristics of a seismic trace over specific intervals or formation layers in the earth. Seismic attributes can provide as much information as possible for integration of the subsurface structure and prediction of the presence and location of hydrocarbons. Seismic attributes have commonly been used for hydrocarbon prospect identification and risking, hydrocarbon play evaluation, reservoir characterization, and the like. An advantage of the seismic attributes is that they can predict at and away from wells, while still honoring well data. Often, predictions are more detailed than simply interpolating well data. There have been a number of distinct seismic attributes which have been calculated both from seismic data and their transforms. However, the use of these attributes to integrate the subsurface structure and predict the reservoir is a problem which has, so far as is known, not been adequately addressed.